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https://olympias.lib.uoi.gr/jspui/handle/123456789/19628Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Poulos, M. | en |
| dc.contributor.author | Rangoussi, M. | en |
| dc.contributor.author | Alexandris, N. | en |
| dc.contributor.author | Evangelou, A. | en |
| dc.date.accessioned | 2015-11-24T19:00:59Z | - |
| dc.date.available | 2015-11-24T19:00:59Z | - |
| dc.identifier.issn | 1463-9238 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/19628 | - |
| dc.rights | Default Licence | - |
| dc.subject | Adult | en |
| dc.subject | Alpha Rhythm | en |
| dc.subject | Anthropology, Physical/*methods | en |
| dc.subject | Beta Rhythm | en |
| dc.subject | Electroencephalography/*methods | en |
| dc.subject | False Negative Reactions | en |
| dc.subject | False Positive Reactions | en |
| dc.subject | Female | en |
| dc.subject | Fourier Analysis | en |
| dc.subject | Humans | en |
| dc.subject | Male | en |
| dc.subject | Medical Informatics Applications | en |
| dc.subject | Medical Informatics Computing | en |
| dc.subject | Middle Aged | en |
| dc.subject | *Neural Networks (Computer) | en |
| dc.subject | Patient Identification Systems/*methods | en |
| dc.subject | Pedigree | en |
| dc.subject | Sensitivity and Specificity | en |
| dc.subject | *Signal Processing, Computer-Assisted | en |
| dc.subject | Theta Rhythm | en |
| dc.title | On the use of EEG features towards person identification via neural networks | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.secondary | http://www.ncbi.nlm.nih.gov/pubmed/11583407 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικής | el |
| heal.publicationDate | 2001 | - |
| heal.abstract | Person identification based on spectral information extracted from the EEG is addressed in this work a problem that has not yet been seen in a signal processing framework. Spectral features are extracted non-parametrically from real EEG data recorded from healthy individuals. Neural network classification is applied on these features using a Learning Vector Quantizer in an attempt to experimentally investigate the connection between a person's EEG and genetically specific information. The proposed method, compared with previously proposed methods, has yielded encouraging correct classification scores in the range of 80% to 100% (case-dependent). These results are in agreement with previous research showing evidence that the EEG carries genetic information. | en |
| heal.journalName | Med Inform Internet Med | en |
| heal.journalType | peer-reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) - ΙΑΤ | |
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